OpenRaiser/Intern-Atlas huggingface dataset:
Identification of chicken egg fertility using SVM classifier based on first-order statistical feature extraction
Venue: arXiv cs.CV 2022
Year: 2022

This study aims to identify chicken eggs fertility using the support vector machine (SVM) classifier method. The classification basis used the first-order statistical (FOS) parameters as feature extraction in the identification process. This research was developed based on the process's identification process, which is still manual (conventional). Although currently there are many technologies in the identification process, they still need development. Thus, this research is one of the developments in the field of image processing technology. The sample data uses datasets from previous studies with a total of 100 egg images. The egg object in the image is a single object. From these data, the classification of each fertile and infertile egg is 50 image data. Chicken egg image data became input in image processing, with the initial process is segmentation. This initial segmentation aims to get the cropped image according to the object. The cropped image is repaired using image preprocessing with grayscaling and image enhancement methods. This method (image enhancement) used two combination methods: contrast limited adaptive histogram equalization (CLAHE) and histogram equalization (HE). The improved image becomes the input for feature extraction using the FOS method. The FOS uses five parameters, namely mean, entropy, variance, skewness, and kurtosis. The five parameters entered into the SVM classifier method to identify the fertility of chicken eggs. The results of these experiments, the method proposed in the identification process has a success percentage of 84.57%. Thus, the implementation of this method can be used as a reference for future research improvements. In addition, it may be possible to use a second-order feature extraction method to improve its accuracy and improve supervised learning for classification.
ValueNetQP: Learned one-step optimal control for legged locomotion
Venue: arXiv cs.RO 2022
Year: 2022

Optimal control is a successful approach to generate motions for complex robots, in particular for legged locomotion. However, these techniques are often too slow to run in real time for model predictive control or one needs to drastically simplify the dynamics model. In this work, we present a method to learn to predict the gradient and hessian of the problem value function, enabling fast resolution of the predictive control problem with a one-step quadratic program. In addition, our method is able to satisfy constraints like friction cones and unilateral constraints, which are important for high dynamics locomotion tasks. We demonstrate the capability of our method in simulation and on a real quadruped robot performing trotting and bounding motions.
Multi-physics integration platform MuPIF: Application for composite material design
Venue: arXiv cs.HC 2022
Year: 2022

This paper presents the design of the MuPIF distributed, multi-physics simulation platform and its performance in the context of the H2020 Composelector project. The description of MuPIF's model and data interfaces provides implementation and operational details that illustrate how MuPIF is a highly versatile and robust tool for various engineering applications. Its distributed design and the integration of models in a workflow allows MuPIF to simulate real industrial tasks. The design of a composite airplane frame for the Composelector project, focusing on innovative design of composite materials and structures, demonstrates MuPIF's capabilities and ability to be integrated into Business Decision Support Systems.
Degree Sequence Bound For Join Cardinality Estimation
Venue: arXiv cs.DB 2022
Year: 2022

Recent work has demonstrated the catastrophic effects of poor cardinality estimates on query processing time. In particular, underestimating query cardinality can result in overly optimistic query plans which take orders of magnitude longer to complete than one generated with the true cardinality. Cardinality bounding avoids this pitfall by computing a strict upper bound on the query's output size using statistics about the database such as table sizes and degrees, i.e. value frequencies. In this paper, we extend this line of work by proving a novel bound called the Degree Sequence Bound which takes into account the full degree sequences and the max tuple multiplicity. This bound improves upon previous work incorporating degree constraints which focused on the maximum degree rather than the degree sequence. Further, we describe how to practically compute this bound using a learned approximation of the true degree sequences.
Learning Robust Policies for Generalized Debris Capture with an Automated Tether-Net System
Venue: arXiv cs.RO 2022
Year: 2022

Tether-net launched from a chaser spacecraft provides a promising method to capture and dispose of large space debris in orbit. This tether-net system is subject to several sources of uncertainty in sensing and actuation that affect the performance of its net launch and closing control. Earlier reliability-based optimization approaches to design control actions however remain challenging and computationally prohibitive to generalize over varying launch scenarios and target (debris) state relative to the chaser. To search for a general and reliable control policy, this paper presents a reinforcement learning framework that integrates a proximal policy optimization (PPO2) approach with net dynamics simulations. The latter allows evaluating the episodes of net-based target capture, and estimate the capture quality index that serves as the reward feedback to PPO2. Here, the learned policy is designed to model the timing of the net closing action based on the state of the moving net and the target, under any given launch scenario. A stochastic state transition model is considered in order to incorporate synthetic uncertainties in state estimation and launch actuation. Along with notable reward improvement during training, the trained policy demonstrates capture performance (over a wide range of launch/target scenarios) that is close to that obtained with reliability-based optimization run over an individual scenario.
Equilibration Analysis and Control of Coordinating Decision-Making Populations
Venue: arXiv cs.MA 2022
Year: 2022

Whether a population of decision-making individuals will reach a state of satisfactory decisions is a fundamental problem in studying collective behaviors. In the framework of evolutionary game theory and by means of potential functions, researchers have established equilibrium convergence under different update rules, including best-response and imitation, by imposing certain conditions on agents' utility functions. Then by using the proposed potential functions, they have been able to control these populations towards some desired equilibrium. Nevertheless, finding a potential function is often daunting, if not near impossible. We introduce the so-called coordinating agent who tends to switch to a decision only if at least another agent has done so. We prove that any population of coordinating agents, a coordinating population, almost surely equilibrates. Apparently, some binary network games that were proven to equilibrate using potential functions are coordinating, and some coloring problems can be solved using this notion. We additionally show that any mixed network of agents following best-response, imitation, or rational imitation, and associated with coordination payoff matrices is coordinating, and hence, equilibrates. As a second contribution, we provide an incentive-based control algorithm that leads coordinating populations to a desired equilibrium. The algorithm iteratively maximizes the ratio of the number of agents choosing the desired decision to the provided incentive. It performs near optimal and as well as specialized algorithms proposed for best-response and imitation; however, it does not require a potential function. Therefore, this control algorithm can be readily applied in general situations where no potential function is yet found for a given decision-making population.
Benchmarking Deep Reinforcement Learning Algorithms for Vision-based Robotics
Venue: arXiv cs.RO 2022
Year: 2022

This paper presents a benchmarking study of some of the state-of-the-art reinforcement learning algorithms used for solving two simulated vision-based robotics problems. The algorithms considered in this study include soft actor-critic (SAC), proximal policy optimization (PPO), interpolated policy gradients (IPG), and their variants with Hindsight Experience replay (HER). The performances of these algorithms are compared against PyBullet's two simulation environments known as KukaDiverseObjectEnv and RacecarZEDGymEnv respectively. The state observations in these environments are available in the form of RGB images and the action space is continuous, making them difficult to solve. A number of strategies are suggested to provide intermediate hindsight goals required for implementing HER algorithm on these problems which are essentially single-goal environments. In addition, a number of feature extraction architectures are proposed to incorporate spatial and temporal attention in the learning process. Through rigorous simulation experiments, the improvement achieved with these components are established. To the best of our knowledge, such a benchmarking study is not available for the above two vision-based robotics problems making it a novel contribution in the field.
Understanding how people consume low quality and extreme news using web traffic data
Venue: arXiv cs.SI 2022
Year: 2022

To mitigate the spread of fake news, researchers need to understand who visit fake new sites, what brings people to those sites, where visitors come from, and what content they prefer to consume. In this paper, we analyze web traffic data from The Gateway Pundit (TGP), a popular far-right website that is known for repeatedly sharing false information that has made its web traffic available to the general public. We collect data on 68 million web traffic visits to the site over a month period and analyze how people consume news via multiple features. Our traffic analysis shows that search engines and social media platforms are main drivers of traffic; our geo-location analysis reveals that TGP is more popular in counties that voted for Trump in 2020; and our topic analysis shows that conspiratorial articles receive more visits than factual articles. Due to the inability to observe direct website traffic, existing research uses alternative data source such as engagement signals from social media posts. To validate if social media engagement signals correlate with actual web visit counts, we collect all Facebook and Twitter posts with URLs from TGP during the same time period. We show that all engagement signals positively correlate with web visit counts, but with varying correlation strengths. Metrics based on Facebook posts correlate better than metrics based on Twitter. Our unique web traffic data set and insights can help researchers to better measure the impact of far-right and fake news URLs on social media platforms.
Finding Your Way Through the Jungle of Big Data Architectures
Venue: arXiv cs.DB 2022
Year: 2022

This paper presents a systematic review of common analytical data architectures based on DAMA-DMBOK and ArchiMate. The paper is work in progress and provides a first view on Gartner's Logical Data Warehouse paradigm, Data Fabric and Dehghani's Data Mesh proposal as well as their interdependencies. It furthermore sketches the way forward how this work can be extended by covering more architecture paradigms (incl. classic Data Warehouse, Data Vault, Data Lake, Lambda and Kappa architectures) and introducing a template with among others "context", "problem" and "solution" descriptions, leading ultimately to a pattern system providing guidance for choosing the right architecture paradigm for the right situation.
Object Gathering with a Tethered Robot Duo
Venue: arXiv cs.RO 2022
Year: 2022

We devise a cooperative planning framework to generate optimal trajectories for a tethered robot duo, who is tasked to gather scattered objects spread in a large area using a flexible net. Specifically, the proposed planning framework first produces a set of dense waypoints for each robot, serving as the initialization for optimization. Next, we formulate an iterative optimization scheme to generate smooth and collision-free trajectories while ensuring cooperation within the robot duo to efficiently gather objects and properly avoid obstacles. We validate the generated trajectories in simulation and implement them in physical robots using Model Reference Adaptive Controller (MRAC) to handle unknown dynamics of carried payloads. In a series of studies, we find that: (i) a U-shape cost function is effective in planning cooperative robot duo, and (ii) the task efficiency is not always proportional to the tethered net's length. Given an environment configuration, our framework can gauge the optimal net length. To our best knowledge, ours is the first that provides such estimation for tethered robot duo.
Gridiron: A Technique for Augmenting Cloud Workloads with Network Bandwidth Requirements
Venue: arXiv cs.DC 2022
Year: 2022

Cloud applications use more than just server resources, they also require networking resources. We propose a new technique to model network bandwidth demand of networked cloud applications. Our technique, Gridiron, augments VM workload traces from Azure cloud with network bandwidth requirements. The key to the Gridiron technique is to derive inter-VM network bandwidth requirements using Amdahl's second law. As a case study, we use Gridiron to generate realistic traces with network bandwidth demands for a distributed machine learning training application. Workloads generated with Gridiron allow datacenter operators to estimate the network bandwidth demands of cloud applications and enable more realistic cloud resource scheduler evaluation.
MDS-Net: A Multi-scale Depth Stratification Based Monocular 3D Object Detection Algorithm
Venue: arXiv cs.CV 2022
Year: 2022

Monocular 3D object detection is very challenging in autonomous driving due to the lack of depth information. This paper proposes a one-stage monocular 3D object detection algorithm based on multi-scale depth stratification, which uses the anchor-free method to detect 3D objects in a per-pixel prediction. In the proposed MDS-Net, a novel depth-based stratification structure is developed to improve the network's ability of depth prediction by establishing mathematical models between depth and image size of objects. A new angle loss function is then developed to further improve the accuracy of the angle prediction and increase the convergence speed of training. An optimized soft-NMS is finally applied in the post-processing stage to adjust the confidence of candidate boxes. Experiments on the KITTI benchmark show that the MDS-Net outperforms the existing monocular 3D detection methods in 3D detection and BEV detection tasks while fulfilling real-time requirements.
Theoretical Limits of Joint Detection and Estimation for Radar Target
Venue: arXiv cs.IT 2022
Year: 2022

This paper proposes a joint detection and estimation (JDE) scheme based on mutual information for the radar work, whose goal is to choose the true one between target existent and target absence, and to estimate the unknown distance parameter when the target is existent. Inspired by the thoughts of Shannon information theory, the JDE system model is established in the presence of complex white Gaussian noise. We make several main contributions: (1) the equivalent JDE channel and the posterior probability density function are derived based on the priori statistical characteristic of the noise, target scattering and joint target parameter; (2) the performance of the JDE system is measured by the joint entropy deviation and the joint information that is defined as the mutual information between received signal and the joint target parameter; (3) the sampling a posterior probability and cascaded JDEers are proposed, and their performance is measured by the empirical joint entropy deviation the empirical joint information; (4) the joint theorem is proved that the joint information is the available limit of the overall performance, that is, the joint information is available, and the empirical joint information of any JDEer is no greater than the joint information; (5) the cascaded theorem is proved that the sum of empirical detection information and empirical estimation information can approximate the joint information, i.e., the performance limit of cascaded JDEer is available. Simulation results verify the correctness of the joint and the cascaded theorems, and show that the performance of the sampling a posterior probability JDEer is asymptotically optimal. Moreover, the performance of cascaded JDEer can approximate the system performance of JDE system.
A simple model for citation curve
Venue: arXiv cs.DL 2022
Year: 2022

There is considerable interest in the citation count for an author's publications. This has led to many proposals for citation indices for characterizing citation distributions. However, there is so far no tractable model to facilitate the analysis of these distributions and the design of these indices. This paper presents a simple equation for such design and analysis. The equation has three parameters that are calibrated by three geometrical characteristics of a citation distribution. Its simple form makes it tractable. To demonstrate, the equation is used to derive closed-form expressions for various citation indices, analyze the effect of time and identify individual contribution to the Hirsch index for a group.
Computational analyses of the topics, sentiments, literariness, creativity and beauty of texts in a large Corpus of English Literature
Venue: arXiv cs.CL 2022
Year: 2022

The Gutenberg Literary English Corpus (GLEC, Jacobs, 2018a) provides a rich source of textual data for research in digital humanities, computational linguistics or neurocognitive poetics. In this study we address differences among the different literature categories in GLEC, as well as differences between authors. We report the results of three studies providing i) topic and sentiment analyses for six text categories of GLEC (i.e., children and youth, essays, novels, plays, poems, stories) and its >100 authors, ii) novel measures of semantic complexity as indices of the literariness, creativity and book beauty of the works in GLEC (e.g., Jane Austen's six novels), and iii) two experiments on text classification and authorship recognition using novel features of semantic complexity. The data on two novel measures estimating a text's literariness, intratextual variance and stepwise distance (van Cranenburgh et al., 2019) revealed that plays are the most literary texts in GLEC, followed by poems and novels. Computation of a novel index of text creativity (Gray et al., 2016) revealed poems and plays as the most creative categories with the most creative authors all being poets (Milton, Pope, Keats, Byron, or Wordsworth). We also computed a novel index of perceived beauty of verbal art (Kintsch, 2012) for the works in GLEC and predict that Emma is the theoretically most beautiful of Austen's novels. Finally, we demonstrate that these novel measures of semantic complexity are important features for text classification and authorship recognition with overall predictive accuracies in the range of .75 to .97. Our data pave the way for future computational and empirical studies of literature or experiments in reading psychology and offer multiple baselines and benchmarks for analysing and validating other book corpora.
Evolutionary Optimization for Proactive and Dynamic Computing Resource Allocation in Open Radio Access Network
Venue: arXiv cs.NE 2022
Year: 2022

Intelligent techniques are urged to achieve automatic allocation of the computing resource in Open Radio Access Network (O-RAN), to save computing resource, increase utilization rate of them and decrease the delay. However, the existing problem formulation to solve this resource allocation problem is unsuitable as it defines the capacity utility of resource in an inappropriate way and tends to cause much delay. Moreover, the existing problem has only been attempted to be solved based on greedy search, which is not ideal as it could get stuck into local optima. Considering those, a new formulation that better describes the problem is proposed. In addition, as a well-known global search meta heuristic approach, an evolutionary algorithm (EA) is designed tailored for solving the new problem formulation, to find a resource allocation scheme to proactively and dynamically deploy the computing resource for processing upcoming traffic data. Experimental studies carried out on several real-world datasets and newly generated artificial datasets with more properties beyond the real-world datasets have demonstrated the significant superiority over a baseline greedy algorithm under different parameter settings. Moreover, experimental studies are taken to compare the proposed EA and two variants, to indicate the impact of different algorithm choices.
Detecting Ransomware Execution in a Timely Manner
Venue: arXiv cs.CR 2022
Year: 2022

Ransomware has been an ongoing issue since the early 1990s. In recent times ransomware has spread from traditional computational resources to cyber-physical systems and industrial controls. We devised a series of experiments in which virtual instances are infected with ransomware. We instrumented the instances and collected resource utilization data across a variety of metrics (CPU, Memory, Disk Utility). We design a change point detection and learning method for identifying ransomware execution. Finally we evaluate and demonstrate its ability to detect ransomware efficiently in a timely manner when trained on a minimal set of samples. Our results represent a step forward for defense, and we conclude with further remarks for the path forward.
Jamming Detection for IR-UWB Ranging Technology in Autonomous UAV Swarms
Venue: arXiv cs.CR 2022
Year: 2022

Jamming is a form of the Denial of Service (J-DoS) attack. It is a significant threat that causes malfunction in Unmanned Aerial Vehicle systems, especially when used in hostile environments. The attackers mainly operate in the wireless communication environment by following a few preexisting scenarios. In this paper, we propose an idea for a Jamming detection mechanism. The mechanism utilizes the network parameters available to the system and some additional measures to distinguish between bad transmission quality and Jamming to avoid false positive alarms. After detecting a Jamming attack, appropriate countermeasures or mitigation techniques can be applied to keep the system safe.